tests/fpctests_notallin.R

In fpc: Flexible Procedures for Clustering

# This tests a few things that are not run in the examples.

library(fpc)
library(MASS)
library(diptest)
library(mclust)
options(digits=3)

set.seed(4634)
face <- rFace(300,dMoNo=2,dNoEy=0,p=3)
grface <- as.integer(attr(face,"grouping"))
# discrproj(face,grface, clnum=1, method="bc")$units
discrproj(face,grface, clnum=1, method="anc")$units
discrproj(face,grface, clnum=1, method="awc")$units


pamk(face,krange=1:5,criterion="ch",usepam=FALSE,critout=TRUE)

set.seed(20000)
face50 <- rFace(50,dMoNo=2,dNoEy=0,p=2)
pamk(dist(face50),krange=1:5,criterion="asw",critout=TRUE)

x <- c(1,2,3,6,6,7,8,120)
ff8 <- fixmahal(x)
summary(ff8)
  # ...dataset a bit too small for the defaults...
ff9 <- fixmahal(x, mnc=3, startn=3)
summary(ff9)

set.seed(776655)
v1 <- rnorm(100)
v2 <- rnorm(100)
d1 <- sample(1:5,100,replace=TRUE)
d2 <- sample(1:4,100,replace=TRUE)
ldata <- cbind(v1,v2,d1,d2)
fr <- flexmixedruns(ldata,
    continuous=2,discrete=2,simruns=1,initial.cluster=c(rep(1,5),rep(2,45),
                                        rep(3,50)),
                    control=list(minprior=0.1),
                    n.cluster=3,allout=FALSE)
print(fr$optsummary)

dface <- dist(face50)


hclusttreeCBI(face50,minlevel=2,method="complete",scaling=TRUE)

disthclusttreeCBI(dface,minlevel=2,method="complete")

noisemclustCBI(face50,G=1:5,emModelNames="VVV",nnk=2)

distnoisemclustCBI(dface,G=5,emModelNames="EEE",nnk=2,
                        mdsmethod="classical",
                        mdsdim=2)

mahalCBI(face50,clustercut=0.5)

set.seed(20000)
face100 <- rFace(100,dMoNo=2,dNoEy=0,p=2)
cbf <- clusterboot(face100,B=2,clustermethod=speccCBI,showplots=TRUE,k=6,seed=50000)
cbf$nc
cbf$noisemethod
cbf$bootmethod
# suppressWarnings(if(require(tclust))
# print(clusterboot(face100,B=2,clustermethod=tclustCBI,showplots=TRUE,k=5,seed=50000,noisemethod=TRUE)))


complete3 <- cutree(hclust(dface),3)

cluster.stats(dface,complete3,G2=TRUE)

set.seed(55667788)

data(crabs)
dc <- crabs[,4:8]
cmo <- mclustBIC(crabs[,4:8],G=9,modelNames="EEE")
# set.seed(12345)
cm <- mclustBIC(crabs[,4:8],G=9,modelNames="EEE",
                initialization=list(noise=(1:200)[sample(200,50)]))


scm <- summary(cm,crabs[,4:8])
scmo <- summary(cmo,crabs[,4:8])

set.seed(334455)
summary(mergenormals(crabs[,4:8],scm,method="ridge.ratio",by=0.05))
summary(mergenormals(crabs[,4:8],scmo,method="ridge.uni",by=0.05))
# summary(mergenormals(crabs[,4:8],scm,method="diptantrum",by=0.05))
# summary(mergenormals(crabs[,4:8],scmo,method="dipuni",by=0.05))
# summary(mergenormals(crabs[,4:8],scm,method="predictive",M=2))

set.seed(20000)
x1 <- rnorm(50)
y <- rnorm(100)
x2 <- rnorm(40,mean=20)
x3 <- rnorm(10,mean=25,sd=100)
x0 <- cbind(c(x1,x2,x3),y)

prediction.strength(x0,M=10,Gmax=4,
                           clustermethod=noisemclustCBI,
                           classification="qda")

prediction.strength(dist(x0),M=10,Gmax=4,
                           clustermethod=claraCBI,
                           classification="centroids")


set.seed(20000)
xdata <- c(rnorm(10,0,1),rnorm(10,8,1))
clustermethod=c("claraCBI","dbscanCBI")

clustermethodpars <- list()
clustermethodpars[[1]] <- clustermethodpars[[2]] <- list()
clustermethodpars[[2]]$eps <- 2
clustermethodpars[[2]]$MinPts <- 2
cbs <-  clusterbenchstats(xdata,G=3,clustermethod=clustermethod,
    distmethod=rep(TRUE,2),ncinput=c(TRUE,FALSE),scaling=FALSE,
    clustermethodpars=clustermethodpars,nnruns=2,kmruns=2,fnruns=1,avenruns=1,useallg=TRUE)

print(cbs$sstat,aggregate=TRUE,weights=c(1,0,0,0,0,1,0,0,0,1,0,1,1,0,0,1),include.othernc=cbs$cm$othernc)
print(cbs$qstat,aggregate=TRUE,weights=c(1,0,0,0,0,1,0,0,0,1,0,1,1,0,0,1),include.othernc=cbs$cm$othernc)